• N&PD Moderators: Skorpio | thegreenhand

Erowid/BlueLight Neuropharmacology Text

in the sense that they probably know a few things about cells, chemistry etc., but have never heard of the likes of allosteric modulation or don't know that an agonist and antognist are not really 'true opposites
Is that in reference to anything in particular, or just in general? If you have anything you think needs changing give us a specific rewrite...


In regards to the PDSP data, I remember seeing something on MAPs about some due paying for screening for a bunch of hallucinogens, and talking about "receptor space" well, either way, the data on the propylthio- PEAs were never published either in journals nor on the PDSP database.
 
Just in general, it might be a good idea to have a 'suggested further reading' section and/or some links to maybe some introductory online academic resources.

Yea I believe the receptor data was part of the 'receptor space' project. But I'll be darned if I can find it anywhere... It would sure be interesting to see though.
 
Amphetamines and the monoamine transporter

Introduction
“Amphetamine” is a term given to a structurally related class of compounds sharing the alpha-methyl phenethylamine backbone. Although the amphetamines contain an array of pharmacologically distinct molecules, the classical amphetamine action is to raise the extracellular concentration of monoamine neurotransmitters (dopamine, serotonin and noradrenaline). Exactly how amphetamines cause the increase in monoamines is a complex, probably multifactorial mechanism, which will be reviewed here.

Facilitated exchange diffusion
Amphetamines cause a massive increase in extracellular monoamines. Unlike reuptake inhibitors such as cocaine, which might cause a 200-400% increase in free monoamines, amphetamines can cause increase up to and over 1000%. This increase seems largely to be dependent on a group of integral membrane proteins collectively called the monoamine transporters. The monoamine transporter comes in four flavors, the dopamine, serotonin, noradrenalin and vesicular monoamine transporters, which are selectively expressed on dopaminergic, serotonergic, noradrenergic neurons and monoamine containing vesicles selectively. Each transporter is relatively selective for the monoamine that it gets it name from, though not exclusively. Exactly how monoamine transporters take monoamines from the extracellular space and into the neuron is unclear. Monoamine transporters are 12 transmembrane spanning proteins which are believed to function a homomultimers, and as well as their neurotransmitter binding site, they have Na+ and Cl- ion binding sites (figure 1). They transporter is generally believed to function by Na+ and Cl- first binding to the extracellular face of the protein, and then the neurotransmitter binds extracellularly. This binding somehow causes the protein to change conformation so that the extracellular binding sites are facing into the cell, the ions and neurotransmitter dissociate and hence are transported into the cell (figure 2).

6-DAT.jpg

Figure 1. Structure of the monoamine transporter, Red reflects negative region, and blue positive. Intracellular face at the bottom of the page from Ravna et al., 2003

amphetamine_cycle.gif

Figure 2. Model of monoamine transporter function

The first mechanism used to explain amphetamine function was the so-called “facilitated exchange diffusion” model by Paton, 1973. In this model, amphetamine binds to extracellular neurotransmitter binding site of the transporter, causing the transporter to move the amphetamine molecule into the intracellular space, leaving the neurotransmitter binding site open in the intracellular face (for at least as long as it takes the transporter to flip back to facing extracellularly). Over the entire cell, amphetamine should cause an increase in the proportion of intracellular facing transporters, and on the whole increase the rate of reverse transporter (that is transport of neurotransmitter out of the cell). The source of the monoamines which are susceptible to reverse transport is unclear. Obviously, these monoamines must be in the cytosol of the cell, and not contained in vesicles, however, whether these neurotransmitters must first be displaced from vesicles by facilitated exchange diffusion through the vesicular monoamine transporter, or whether free neurotransmitter levels inside the cell are enough to support reverse transporter is unknown.

Facilitated exchange diffusion is supported by a large number of relatively correlative observations. In order for the neurotransmitter to bind to the transporter, sodium must bind as well and in accordance with that amphetamine-induced reverse transport depends on both extracellular Na+ (so that amphetamine can be transported into the cell) and intracellular Na+ (so that the neurotransmitter can be transporter out) (Schmitz et al., 2001) and infact an increase in intracellular sodium is enough to cause reverse transport (Khoshbousie et al., 2003). Another observation that this author makes that at the very least doesn’t disprove facilitated exchange diffusion is that the affinity of various amphetamines for the extracellular binding site is tightly correlated with their ability to release monoamines (figure 3) (this seems to indicate that binding to the extracellular binding site, and hence probably transport into the cell, is all that is required to induce reverse transport). Another consequence of the facilitated exchange diffusion model is that amphetamines would compete for neurotransmitters at the transporter, and as a result inhibit monoamine uptake. Indeed, it is often sited that amphetamines work by inhibiting reuptake, but the actual contribution of amphetamine-induced reverse transport and reuptake inhibition in regards to the increase in free monoamines is hard to calculate but it has been estimated that the majority of the increase in monoamines is due to reverse transport (Schmitz et al., 2001). This conclusion can be easily seen to be true as the maximum increase in free monoamines in the brain caused by amphetamines is well over 1000%, while cocaine causes a maximum increase in extracellular dopamine around 500%.

amphcorrelation.gif

Figure 3. A tight correlation between uptake inhibition and release between various amphetamines

There are however several observations that bring the simplicity of facilitated exchange diffusion, which have produced other theories, discussed below.

Channel Mode
Interestingly, it has been shown, that under the right conditions, cells expressing monoamine transporters can display large current events which are blocked by drugs which block monoamine transporters and are coincident with very large effluxes of monoamine neurotransmitters. These channel like events have been shown to contain on the order of 10,000 molecules of neurotransmitter, released over a few milliseconds at the most (Kahlig et al., 2005). In order to give this number some scale, this is approximately the same number of neurotransmitter molecule inside a vesicle. Amphetamine drastically increases the rate of these channel like events. It is worth noting that Kahlig et al., reports that these events only happen when neurons are held at massively depolarized potentials (>+40mV), so these channel like events are only likely to happen during the peak of an action potential.


2nd Messanger systems
Monoamine transporters have numerous sites which can be phsophorylated and this phosphorylation seems to be play an important role in amphetamine-mediated reverse transport. Specifically the activity of protein kinase C (PKC) seems to regulate transporter activity, for instance activating PKC is enough to induce monoamine transporter-dependent monoamine release, and it has been reported that PKC inhibition blocks amphetamine mediated dopamine release (Kantor et al., 2001). Furthermore, removal of a small section of the N-terminus of the dopamine transporter, or modification of this sections of the amino acid sequence so that it can not be phsophorylated (serine residues replaced with alanine) reduced amphetamine mediated dopamine release by 80% while leaving dopamine uptake unchanged. (Khoshbouei et al., 2004). Exactly how amphetamine lead to an activation of PKC is unclear, but may revolve around an alteration of intracellular Na+ homeostasis (due to Na+/amphetamine co-transport) and an influx of Ca2+ through the Na+/Ca2+ co-transporter.

Monoamine Oxidase Inhibition
It is often claimed that amphetamines work, at least in part, by inhibiting monoamine oxidase (MAO), the intracellularly expressed enzyme responsible for the break down of the monoamine neurotransmitters. The importance of this effect in general is probably minimal at best, as MDMA, methamphetamine and amphetamine are usually reported to need a concentration of 10-100µM to inhibit MAO-A 50% (most amphetamines are relatively MAO-A selective). However these drugs need concentrations 1000x lower than that to significantly effect monoamine release. While some people suggest that as MAO is an intracellular enzyme, and amphetamines are probably highly concentrated inside cells due to their transport by the monoamine transporters (though this has never been directly measured), monoamine oxidase inhibitors only cause a modest increase in free monoamine levels (~200%), and hence at best it is the MAOI effects of amphetamines must be minimal.


Other mechanisms
Many other mechanisms have been used to explain amphetamine actions, such as the weak base effect. In this theory the accumulation of amphetamines (which are weak bases) in synaptic vesicles, increases the vesicular pH to a point where the vesicular matrix, which holds neurotransmitters in a stable state breaks down, and causes neurotransmitters to leak into the cytosol of the cell. This increase of free intracellular monoamines favors reverse transport and leads to monoamine release. Another idea is that inward amphetamine transport which is driven by Na+ leads to a substantial inward Na+ current, depolarizing the cell, and leading to classical vesicular release. The contribution of this effect is largely unknown, but seems unlikely as vesicular release has largely been disproved as a mechanism of amphetamine-induced monoamine release (amphetamine-induced release is independent of extracellular Ca2+ and is insensitive to toxins which disrupt vesicular exocytosis).

Conclusion
Exactly how amphetamines cause monoamine release is still not clear. It seems to be caused by a reversal of monoamine transport through monoamine transporters. This amphetamine-induced transporter-mediated reverse transport has been shown to be largely dependent on PKC-mediated phosphorylation of the N-terminus of the transporter, and facilitated exchange diffusion. Other effects, such as reuptake inhibition, MAO inhibition and a facilitation of the channel like mode of the transporter may play small roles in mediating amphetamine-induced increase in monoamine neurotransmitter levels.


Further Reading
An excellent, complete historic review:
Sulzer D, Sonders MS, Poulsen NW, Galli A.
Mechanisms of neurotransmitter release by amphetamines: a review.
Prog Neurobiol. 2005 Apr;75(6):406-33

References

K.M. Kahlig, F. Binda, H. Khoshbouei, R.D. Blakely, D.G. McMahon, J.A. Javitch and A. Galli.
Amphetamine induces dopamine efflux through a dopamine transporter channel
Proc. Natl. Acad. Sci. U.S.A. 102 (2005), pp. 3495–3500

L. Kantor, G.H. Hewlett, Y.H. Park, S.M. Richardson-Burns, M.J. Mellon and M.E. Gnegy.
Protein kinase C and intracellular calcium are required for amphetamine-mediated dopamine release via the norepinephrine transporter in undifferentiated PC12 cells
J. Pharmacol. Exp. Ther. 297 (2001), pp. 1016–1024

H. Khoshbouei, H. Wang, J.D. Lechleiter, J.A. Javitch and A. Galli.
Amphetamine-induced dopamine efflux. A voltage-sensitive and intracellular Na+-dependent mechanism
J. Biol. Chem. 278 (2003), pp. 12070–12077.

H. Khoshbouei, N. Sen, B. Guptaroy, L. Johnson, D. Lund, M.E. Gnegy, A. Galli and J.A. Javitch
N-terminal phosphorylation of the dopamine transporter is required for amphetamine-induced efflux
PLoS Biol. 2 (2004), p. E78

D.M. Paton
Mechanism of efflux of noradrenaline from adrenergic nerves in rabbit atria
Br. J. Pharmacol. 49 (1973), pp. 614–627

A. W. Ravna, I. Sylte, S. G. Dahl
Molecular model of the neural dopamine transporter
J Comp-Aid Mol Des 17: 367–382, 2003.

Y. Schmitz, C.J. Lee, C. Schmauss, F. Gonon and D. Sulzer
Amphetamine distorts synaptic dopamine overflow: effects on D2 autoreceptors, transporters, and synaptic vesicle stores
J. Neurosci. 21 (2001), pp. 5916–5924.
 
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What is a Protein?
Proteins are a certain type of biopolymers/biomacromolecules (molecules that are made out of very many components and therefore have a large molecular weight).

Proteins are made out of amino acids, joined together in a generally linear chain. There are 20 different amino acids that proteins can be made from. The length of those "polypeptid chains" can be as short as less than 20 amino acids or as long as tens of thousand amino acids. This enables the proteins to have a large variety of functions. To mention just a few of them, they can function as neurotransmitters, enzymes, receptors or ion channels, and in fact mediate and control near every function of the body.

Every amino acid has slightly different proterties: some are water soluble while others are hydrophobic, some easily function as catalytic centres (that is, they are places for chemical reactions to take place), some can easily be joined to other amino acids, forming bonds between distant places on the protein, or even other proteins. The sequence of amino acids which make up the protein are tightly regulated by messenger ribonucleic acid (mRNA), another biopolymer which is copied from, and is essentially a mirror image of DNA. If the body was a construction projection, DNA can be thought of as the manual, mRNA is a photocopy of the manual, and proteins are the finished projected.

The important thing about proteins is that they hold their shape. Although proteins are produced like long rope, they fold up as they are produced into shapes dictated by the amino acids that make them up, hence all proteins of the same structure should form into the same shape. Hydrophilic amino acids will form to face into the aqueous composition of the cell, while hydrophobic amino acids will stick next to other hydrophobic amino acids, often forming the interior of the protein. Some proteins are produced to be partially buried in, or transit the membrane, here, hyrdophobic amino acids will form the part of the protein which sit in the membrane.

While proteins are rigid, they can subtlety change their shape, or conformation. These changes may happen spontaneously, may be induced by chemical changes to the protein, or even by the distribution of electrical charge which surrounds the protein.
 
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Oh Christ, the joy I am getting out of seeing this text's slow and arduous birth. Can't wait till it's finished. Keep up the good work, BilZ0r and friends!
 
Learning and Memory on a cellular level

Even some of the most simple animals with neural structures too small to call brains, can learn from their experiences. Sea snails (animals with only a few thousand neurons) can associate neutral stimuli (those which produce no response) with noxious ones, if they repeatedly happen together and learn to respond to the neutral one as if it were the noxious one. Importantly, they can also unlearn the response, if a previously neutral stimulus is no longer paired with the noxious stimulus. Brains can also learn to pair neutral stimuli with rewarding stimuli, the classical example being Pavlov’s dogs, who learned to pair the sound of Pavlov entering his lab, with the food he gave them (he was studying the effects of food on salivation, however to his annoyance, the dogs began to salivate BEFORE he gave them the food). Interestingly, this so called Pavlovian conditioning is dependent on how well the neutral stimulus predicts the rewarding stimuli. If Pavlov had entered his lab regularly without feeding the dogs, it is likely that the dogs would have never learned to salivate to the sound of his footsteps.

Believe it or not, these learning situations are the comparatively complicated one as far as the neuroscientists is concerned. Imagine the situation where an experimental animal is exposed to a light that consistently precedes (and hence predicts) a tone, this is called sensory preconditioning. Following a Pavlovian style learning situation, a puff of air into the eye produces a blink, and if the puff is consistently paired with a tone, the subject will learn to blink just to the tone. Now if the animal is exposed to the light, it will blink, even though the tone has never been paired with the puff.

All of these experimental results may seem obvious, but they show some very important things. Firstly, it was proposed by Hebb that if a neuron A consistently takes part in firing (i.e. fires at the same time as, and is connected to) neuron B, then the connectivity between neuron A and B will increase. What is interesting about sensory preconditioning is that obvious connectivity between the two neutral sensory stimuli formed just by pairing them together, supporting Hebbs theory. Hebbian principles were further engrained when it was shown that simply by strongly exciting a set of neuronal inputs into a population of neurons, the neuronal inputs became more efficient at this excitation, importantly this potentiation lasts for extremely long period (recorded for over 1 year). This is due a series of changes in the synapse, both pre- and postsynaptically, including an increase in glutamate release and an increase in AMPA receptors in the post-synaptic membrane. This long term potentiation (LTP) has been used as a model for memory since it was discovered over 30 years ago, and has stood robustly against most challenges. Specifically drugs and genetic modulation which prevent the formation or maintenance of LTP also prevent learning.

Reward relating learning (like like a rat pressing a lever for food) also has interesting cellular mechanisms, animals will not learn to pair a neutral stimulus with a food reward if their dopamine receptors are blocked, likewise, if dopaminergic neurons are destroyed the association can not be made. Dopamine cells are active by natural and drug induced rewards. Interestingly, animals will learn to do almost anything for direct stimulation of dopamine neurons. Some people see this as evidence that dopamine directly mediates pleasure, but experiments in the 1960s where humans were given the ability to directly stimulate their own dopamine neurons didn’t report extreme pleasure, though the would constantly activate their dopaminergic neurons (Heath, 1972). Furthermore, if an animal is trained to press a lever to stimulate their dopaminergic neurons, and another animal receives dopaminergic stimulation when the first does, the second animal does not show signs of pleasure, and can even show signs on distress. If this leaves one a bit confused about the role of dopamine, consider this, there are many places in the brain where it has been reported that dopamine massively facilitates the induction of LTP, indeed, in projections from the cortex to the striatum, LTP style stimulation actually produces a suppression in the power of the input neurons from the cortex into the striatum except when dopamine is present . While in the presence of dopamine, a large increase in synaptic strength in generated (Reynolds et al., 2001). The striatum, and especially the ventral striatum (AKA the nucleus accumbens) has been highly associated with the “rewarding” (i.e. refinforcing) properties of natural stimuli and addicting drugs.

This allows us to construct a model of the plasticity of synapses (at least in the striatum), if neuron A (cortical) and neuron B (striatal) are active out of synchrony then there is no change in synaptic strength while if they are active together, without dopamine the synaptic strength decreases. Finally, if neuron A and B are active together in the presence of dopamine, the synaptic strength increases. Although the exact functional role of this corticostriatal dopamine-dependent synaptic plasticity is unclear one can form a reasonable hypothesis. The cortex is activated by sensory stimuli while the striatum (which receives the majority of it’s input from the cortex) is involved in, and active during, movement. Hence a particular sensory stimuli activates a particular area of cortex and cortico-striatal projections, while, a particular behaviour leads to certain striatal neurons being active. If this combination of cortical activity (stimuli input) and striatal activity (behavioral output) produces a reward (dopamine) then the corticostriatal system that was active during this state is strengthened. Addictive drugs, which cause an inappropriate release of striatal dopamine lead to an aberrant corticostriatal state, where cortical neurons which code for drug associate stimuli lead to drug taking behavior.

This becomes even more interesting when one more closely considers the activity of dopaminergic neurons in an awake behaving animal. While on the surface dopaminergic neurons seem to respond to rewarding stimuli, careful examination shows the more closesly reflect the expectancy (or lack thereof) of reward. Dopamine release is most strongly induced by unexpected primary rewards (e.g. food, water), however, if the reward is preceded by a predictive stimuli (e.g. a tone), dopamine release will be shifted to being released by the tone. However, if the tone continually predicts the reward, eventually dopamine release will wane. Likewise, if an unpredicted reward happens regularly, the animal will cease to release dopamine to it's presentation. You can see that if an animal expects a reward, dopamine will not heavily release. However, there is usually a basal level of dopamine being released and if an expected reward is denied, this basal level will drop to zero. Hence, you can see that dopamine release acts as prediction error signal, if the animal recieves an unexpected reward, a large increase in dopamine is induced while if it correctly or incorrectly predicts a reward, dopamine stays at the basal level or decreases respectively.

This shows us how reward related learning can be unlearned, that a pause in dopamine causes corticostriatal synapses to weaken, and the behaviour that was not rewarded is lessened. (theory reviewed by Contreras-Vidal and Schultz, 1999)

While these hypothesis is certainly a vast over simplification, the basic rules are probably true. It is worth noting that not all research groups show that dopamine increase the strength of synapases, and that it can also decrease them (probably dependent on which dopamine receptors are activated (Centonze et al., 2001). However still, dopamine is modulating synaptic plasticity in a reward dependent fashion. This dopamine-mediated synaptic remodelling helps associations form between stimuli and behavior that lead to reward. When this system is hijacked by addictive drugs an aberrant association which encourages drug taking is formed, and it is formed as concretely as any other memory.


References

Centonze D, Picconi B, Gubellini P, Bernardi G, Calabresi P. (2001)
Dopaminergic control of synaptic plasticity in the dorsal striatum.
Eur J Neurosci. 2001 Mar;13(6):1071-7.


Contreras-Vidal JL, Schultz W. (1999)
A predictive reinforcement model of dopamine neurons for learning approach behavior.
J Comput Neurosci. 6(3):191-214

Heath, R. G. (1972). Pleasure and brain activity in man. Deep and surface electroencephalograms during orgasm. Journal of Nervous and Mental Disease, 154(1):3-18

Reynolds JN, Hyland BI, Wickens JR. (2001). A cellular mechanism of reward-related learning.
Nature. 413(6851):67-70
 
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Note: I need people to rip into that one extra hard, if you see any problem with it, please tell me.
 
Hey Bil, I'm not going to be able to make a dent in learning in memory - not my field. I'm gonna post my recent review on receptor signalling networks and the 'receptosome concept,' which you may edit to your heart's content for appropriate content. PS, what ever happened about submitting that article I wrote to Erowid?
 
“G-protein coupled receptors: part of a network of signalling machinery”

Here it is, in all its glory. Its a little ugly, and the figures won't come through i don't think, so drop me a line ( [email protected] preferably) if you want to see the .doc

“G-protein coupled receptors: part of a network of signalling machinery”

The section on “G-protein coupled receptors” (GPCRs) in the popular undergraduate level text book “Pharmacology” by Rang, Dale, Ritter & Moore [1], presents a relatively one-dimensional view of GPCR signalling. A neat diagram sums up the events that occur on binding of a ligand to its cognate GPCR: ligand binding attracts a GTPase (‘g’-) protein to the receptor, and the GDP bound to the g-protein is swapped for a GTP. The now-activated subunits of this heterotrimeric g-protein then dissociate and activate another molecule in the signalling cascade, such as adenylyl cyclase, which subsequently produces ‘second messenger’ molecules. These second messengers are responsible for activation of other downstream effectors, and the signal that began with a ligand binding to the GPCR is propagated.
In contrast, even a cursory glance at any recent review [2,3,4] on the subject of GPCR signalling will reveal to the reader that the textbook model of GPCR signalling is woefully inadequate. Rather, these reviews present the concept that a whole network of interacting proteins and biomolecules nucleated around scaffolding proteins are involved in what was once thought to be a relatively simple, linear transduction process. These protein networks are involved with fine-tuning and regulation of every facet of GPCR function. The ‘receptosome’ concept, that whole networks of molecules are spatially compartmentalised into plasma membrane microdomains such as caveolae and other lipid rafts is integral to all recent models of how GPCR signalling is effected.
In this review I will discuss how each receptosome exists as a self-contained, functional signalling unit, and the importance of spatial compartmentalisation of GPCR signalling machinery. Important experimental observations which lead to the invalidation of older GPCR signalling dogma and forced reconsideration of the whole signalling paradigm will be included. Attention will be paid to the roles that particular groups of proteins play in receptosomes, and the modality of their interactions with other receptosome proteins. Finally I will discuss some of the methodology that is currently being used to determine these interactions and their importance to aspects of GPCR signalling.

The old GPCR signalling dogma:

The older “1-dimensional” (meaning that signal transduction follows a defined, stepwise path, as opposed to three dimensional networks of interactions) model of GPCR signalling has been superceded by models like the ‘receptosome’ model described in this review. Essentially, too many contradictions of and paradoxes in the old model arose from experimental observations. Some of the observations that forced the creation of a newer more comprehensive model are detailed below.
Old dogma states that a specific ligand binds to its cognate GPCR, which then undergoes a conformational change, such that its cytosolic domain develops high affinity for one particular G-protein family and subtype, which it recruits. The activated GPCR activates the recruited G-protein by functioning as a guanine nucleotide exchange factor, exchanging G-protein bound GDP for GTP. The activated G-protein then splits into its alpha and beta-gamma subunit components, which activate secondary targets. G-alpha usually modulates the activity of a second messenger producing enzyme, such as activation of phospholipase C in the case of Gq-alpha, or activation of adenylyl cyclase by Gs-alpha. The second messenger activates second messenger dependent kinases which proliferate the signalling cascade. Additionally, there is room in the one-dimensional model for G-protein Receptor Kinase (GRK) mediated phosphorylation and arrestin mediated desensitisation, which is why although these molecules were discovered relatively early on, they did not push for creation of a new model. [1,3,4]

Organisation of GPCR signalling machinery

One of the fundamental concepts of biochemistry is that the proteins that comprise the majority of cellular machinery interact with each other as huge networks of multiprotein complexes, with specific chemical affinities determining the strengths of these interactions [1,5]. Thus when a ligand binds to its cognate GPCR, a conformational change is induced in the GPCR which creates a chemical site for which specific G-protein(s) have high affinity. Additionally, according to the laws of mass action, the magnitude and rate of chemical interactions and reactions are heavily dependent on the concentrations of the reactants. This raises a fundamental problem with the classical one-dimensional theory of GPCR signalling as can be found in most generic pharmacology textbooks: if one looks at the average concentration of each protein component involved in GPCR signalling, it is far too low to possibly account for the rapidity of the biochemical response to receptor agonism [5]. Kinetics of the protein-protein interactions required to form a signalling cascade must therefore be simply too unfavourable for any agonist directed response to occur if these proteins were randomly or even uniformly distributed across the plasma membrane or cytosol. This appears to be a massive flaw in the old model of GPCR signalling.
Several more flaws can be identified when the old model is compared with recent observations about the characteristics of GPCR signalling.

The old dogma of “1 g-protein couples to one GPCR” has been comprehensively disproved – in fact GPCRs more often than not couple to more than one G-protein [6]. This has significant ramifications for the signalling pathways activated by a particular GPCR. One particularly illuminating example of this observation is that the thyrotropin receptor is able to couple to all four major G-protein families [6]. Other experiments have shown that the majority of GPCRs have at least some affinity for each type of G-protein: therefore the preference for activation of a particular G-protein subtype actually lies on a continuum. In light of this concept, it is possible to infer that it is possible to describe GPCR interactions with particular G-proteins statistically: for example, a particular GPCR may interact with G-protein X 90% of the time, G-protein Y 9.99% of the time and G-protein Z a biochemically negligible 0.01% of the time. These statistics would be based on the chemistry of the interaction sites on the GPCR and G-proteins X, Y & Z. Interaction of the GPCR with G-protein X is obviously the most thermodynamically favourable binding interaction in a mixture of the four proteins at equilibrium.

A further observation that can not be integrated with the old model of GPCR signalling is that agonism by different ligands induces GPCRs to have different affinities for particular G-proteins. The paper “Opioid agonists differentially regulate Mu-opioid receptors and trafficking proteins in vivo” [7] is a good example of how different agonists can induce different biochemical responses in the cell. It is likely that the mechanism for this involves the two agonists used, morphine and etorphine, inducing different receptor conformations, and therefore recruiting different groups of G-proteins to the Mu-opioid receptor.
It is also possible, however, that this effect is not actually mediated by G-proteins at all, and involves direct interaction of other non-G-protein signalling machinery at the Mu receptor. Models have been suggested in which particular receptors may have a number of different conformations which they can assume, and different agonist ligands thermodynamically stabilise particular conformations, which each have a set of G-proteins they activate to different extents. This is a discrete model – there are a defined number of conformations that a receptor can take, and the potency of the agonist to induce that conformation and therefore the overall activity of the drug at the receptor, depends on the degree of thermodynamic stabilisation of that conformation [8]. Alternately, it is possible to imagine a continuous model, where each agonist induces an individual receptor conformation, which alters the G-protein coupling of the receptor and therefore the properties of the signal induced by that agonist. It is even possible to amalgamate these two theories, and conceptualise a model where each different agonist does continuously induce a different receptor conformation, but there are ‘peaks’ in agonist affinity and efficacy which correspond to stabilisation of particular, discrete conformations [9]. Needless to say, a “one GPCR binds to one G-protein” model is completely unable to account for any of these ideas.

Another observation that has forced progression from the older GPCR signalling dogma is that G-protein coupling is not necessarily required for biochemical responses to receptor agonism [10]. Following receptor activation and subsequent G-protein activation, the GPCR is often phosphorylated by a G-protein coupled receptor kinase (GRK,) [10] (or sometimes a by second messenger dependent kinases,) [11] and it is this chemical modification that creates a binding site on the GPCR for a group of proteins called arrestins, which attache to the GPCR and blocks any further coupling to G-proteins, in effect causing the cessation of G-protein mediated signalling [10]. It has been shown, however, that Beta-arrestin may act as a scaffolding molecule and serve to recruit other non-G-protein related signalling machinery. Experiments have shown that arrestin-2 can recruit the tyrosine kinase Src by binding to its SH3 domain, and can also activate MAP kinase pathways. Other experiments showed binding of JNK3 and ASK1, which is a JNK kinase kinase. Thus GPCRs can activate MAPK and tyrosine kinase pathways via their interaction with arrestin proteins [11]. Recent studies have shown an interaction between activated beta2adrenoreceptors (B2AR) and Src which is increased by overexpression of beta-arrestin. Additionally, inhibition of beta-arrestin binding to either B2AR or Src attenuates B2AR mediated activation of MAPK. [10]
Beta-arrestin has also been implicated in regulation of receptor trafficking and endocytosis by its interaction with the heavy chain of clathrin and the clathrin adaptor protein AP2 [10]. These observations of arrestin molecules as scaffolds that nucleate assembly of non-G-protein mediated signalling processes add further detract from old GPCR signalling dogma.

Described above is a series of experimental observations that obligatorily invalidate the one-dimensional model of GPCR signalling, while at the same time building the concept of GPCR signalling as involving a whole network of interacting proteins, with some acting as nodes and scaffolds onto which other proteins nucleate, while others are involved in fine tuning and regulation of the signalling machinery, and still others involved in the trafficking and regulation of the receptors themselves. The most important feature of a newer model of GPCR signalling would have to incorporate the principles of biochemical kinetics and concepts such as collision theory. If all the protein components required for GPCR signalling were to be randomly or even uniformly distributed throughout the cytosol and plasma membrane, the observed rapid response of GPCRs to agonism could not possibly occur. Thus, a new model must include a spatial dimension. The components must be spatially organised such that the biochemistry is actually possible. In answer to this requirement, the literature is packed with reviews and papers documenting the existence of membrane microdomains, or lipid rafts, such as caveolae, in which many of the signalling components and receptors are often congregated.

Caveolae are small (50-100nm) invaginations in the plasma membrane of cells, and are considered to be a subclass of lipid rafts. The lipid composition of caveolae includes characteristically high levels of cholesterol and sphingolipids, along with caveolins, a group of proteins which comprises three isoforms: cavelolin-1, caveolin-2 and caveolin-3. It is generally accepted that caveolae will form if a cell expresses caveolin-1, or in the case of striated muscle myocytes, caveolin-3. Thus, while the plasma membranes of most or all cells contain lipid rafts, only some cells contain caveolae. A 2003 paper in the Journal of Neurochemistry [12] gives a good example of a GPCR being localised to caveolin membrane fractions, and shows “molecular and functional association of mGluR1a receptors with caveolins.” The study demonstrates that agonistic activation of mGluR1a receptors increased ERK phosphorylation in low density caveolin enriched membrane fractions, but not in high density membrane fractions containing no caveolins. Also mentioned in the study was the observation that mGluR1 heterodimerizes with adenosine A1 receptor and calcium sensing receptor; all three of these proteins localise to caveolin rich membrane microdomains [12].
Another example of the role of localisation of receptors and signalling machinery to caveolae is the comparison of Beta1-adrenoreceptor (B1AR) and B2AR signalling in cardiac myocytes. B2ARs activate adenylyl cyclase 6 (AC6) with a lower efficacy than B1ARs, and it appears that this is due to rapid translocation of B2ARs out of caveolae and into clathrin coated pits after receptor activation. AC6 is localised strictly to caveolae, and as such when the B2AR is translocated, it can no longer physically contact AC6 to activate it [5].
A concept that is integral to the model of spatial compartmentalisation of signalling proteins into regions such as caveolae and other lipid rafts is the selective expression of certain isoforms of G-proteins and second messenger synthesising enzymes such as adenylyl cyclase (AC) to particular types of raft, or not to any raft at all. There are nine AC subtypes, but not all of them localise to lipid rafts [5]. Therefore, the fact that many different subtypes exist of G-proteins, second messenger synthesising enzymes and other signalling proteins such as Regulators of G-protein Signalling (RGS,) is a way of increasing the diversity of plasma membrane domains and microdomans.
Many proteins which associate with caveolin proteins contain a caveolin- or caveolin-like- binding motif [13]. Le Clerc et al. published a study in 2002 in the journal Endocrinology examining the effect of Angiotensin II Receptor Type 1’s (AIIR1) caveolin-like binding motif (CLBM) (_X_XXXX_XX_, where _ represents an aromatic amino acid residue) on AIIR1’s signalling and trafficking properties. They mutated this binding motif by replacing each aromatic residue with alanine, a small, sterically unintrusive molecule. The mutated receptor was shown to be four-fold less effective at activating phospholipase C, indicating that the functional CLBM is required for proper signalling. The authors proposed that the CLBM could be acting as a site for nucleation of proteins involved in the regulation of function of AIIR1 [13]. A similar study by Tomohiro Yamaguchi and colleagues [14] examined the effects of interaction of endothelin type A and B (ETaR & ETbR, respectively) receptors with caveolin-1. It was found that ETbR only interacted with caveolin-1 in the absence of an agonist, or bound to the antagonist RES-701-1. When endothelin-1 or another antagonist BQ788 were added, the complex dissociated. ETaR, however, bound to caveolin-1 irrespective of whether a ligand was bound or not. Additionally, overexpression of caveolin-1 dramatically increased the amount of ETbR localised to caveolae, while addition of endothelin-1 reduced caveolar localisation. Disruption of caveolae by filipin reduced the effect of endothelin-1 agonism on ERK1/2 phosphorylation [14].

Taken together, the concepts and experimental observations described here provide the framework for a GPCR signalling platform that is heavily based around spatial compartmentalization of a network of interacting components. This has been called a ‘receptosome’ in some publications [4], and it is quite possible that these receptosomes are the functional unit of plasma membrane receptor signalling, like a cell is the functional unit of a tissue. Agnati et al. in a review publication called “On the molecular basis of the receptor mosaic hypothesis of the engram,” suggest that signalling units such as receptosomes form mosaics on the pre- and post-synaptic membranes of synapses, and that these mosaics are the computational entity that actually decodes the neurochemical messages. They move on to suggest that the arrangement of these mosaics of receptosomes could form ‘supramolecular networks’ that store information about the previous activity patterns of the synapse. While it is important to note that not all GPCR related signalling machinery is congregated into lipid rafts, it is likely that the receptosome theory applies to the majority of GPCR signalling, principally because compartmentalisation of signalling proteins makes such good sense kinetically.
Figure 1. Shows the 5HT2c receptor and its interacting proteins forming a receptosome.


Figure 1. Proteins that interact with the 5HT2c receptor: an example of a synaptic receptosome.

Componentry and organisation of the receptosome: GPCRs & GPCR Interacting Proteins

Having identified general features of the receptosome and the logic behind organising signalling machinery this way, this section of the review will discuss the main groups of proteins that are likely to be part of the receptosome network and their functions.
I will address the questions of what these proteins are, where and how they interact with each other, and why these interactions are fundamental to GPCR / heptahelical transmembrane receptor signalling. Several protein-protein interaction domains such as PDZ, SH2 and SH3 domains are common in receptosome proteins, and the roles of these domains in protein interactions will be highlighted where appropriate.

G-protein Receptor Kinases:

It has been observed experimentally for decades that GPCRs undergo desensitisation and subsequent internalisation under repeated agonist stimulation. The first event in this process is usually phosphorylation of the receptor. There are at least two methods which the cell uses to perform this function: phosphorylation by second messenger activated kinases such as Protein Kinase A (PKA), and phosphorylation by non-second messenger dependent G-protein coupled Receptor Kinases, which are specific to activated GPCRs. The former is an example of ‘heterologous’ desensitisation, whereby agonism of one receptor can result in activation of PKA and subsequent phosphorylation and desensitisation of another receptor [1]. This effect is usually weak and short lasting, and the phospho-residue is not a target for arrestin binding. GRK mediated desensitisation is termed ‘homologous,’ since agonism of a receptor induces desensitisation of the same receptor [1]. Unlike phosphoresidues created by PKA or other second messenger activated kinases, GRK will phosphorylate different sites, and these phosphoresidues are targets for arrestin binding. Once arrestin is bound, various events occur, most importantly blockade of GPCR access to G-proteins. It is not the actual phosphorylation event that desensitises the GPCR in this case, but arrestin binding. GRK mediated phosphorylation was first discovered in the context of rhodopsin-dependent visual signalling, and later, beta2adrenergic receptor signalling. Since then, it has been established that the majority of GPCRs are desensitised in this way [15].

Arrestins:

Arrestins have been known to interact with GPCRs for a relatively long time, and their function was not particularly difficult to fit into the classical GPCR signalling dogma. Arrestins bind, as described previously, to GRK-phosphorylated GPCRs, and for a long time it was thought that arrestins were only involved in desensitisation and internalisation [4]. While some GPCRs internalise independent of arrestins, the usual scenario involves the bound arrestin attaching to clathrin – one of the major components of endocytotic machinery. Follow clathrin binding, arrestin acts as a scaffold protein and nucleates several other proteins to form the multiprotein complex that will effect receptor endocytosis. Other proteins identified in this complex include: AP2 (assembly particle-2,) a large (340kDa) protein that binds to the globular domain at the end of each clathrin heavy chain and function to promote clathrin triskelion formation and oligomerization into the cage that coats membrane invaginations to form clathrin coated pits [16], NSF (n-ethylamide sensitive factor), an intracellular trafficking protein, ARF6, an ADP-ribosylation factor and its exchange factor ARNO, which together regulate vesicle budding. Additionally, arrestin-2 can act as signalling intermediates, and attaches to multiple of the tyrosine kinase c-Src, including its SH3 and SH1 domains to activate MAPK pathways [11]. Arrestin-2 also has an ERK1/2 phosphorylation dependent regulation site at residue Ser-412 which modulates c-SRC and GPCR binding [11]. There are three beta-arrestin subtypes: arrestin-1, 2 & 3, each with different binding specificities and signalling functions. Arrestin-1 is specific to the visual GPCR rhodopsin, while arrestin-2 has a much wider GPCR specificity, and while arrestin-1 is dimeric, arrestin-2 exists as a monomer in solution [11]. These varying characteristics of arrestins add to the overall specificity and complexity of GPCR signalling.
Figure 2. shows a schematic of GPCR activation, arrestin mediated desensitisation, internalisation, and degradation or resensitisation. Some arrestin interacting proteins are shown.


Figure 2. The roles of arrestins in GPCR desensitisation, internalisation, degradation and resensitisation [4]

RGS’s:

Regulators of G-protein Signalling, or ‘RGS’ proteins play a crucial role in regulating the function of G-proteins, and therefore in the signalling efficacy of the receptor system. There are, like many other GIPs examined in this review, a number of members of the RGS family, each with their own G-protein subunit specificity. The mammalian RGS family comprises several subfamilies, termed: Rz, R4, RA, R12 and R7, which are classified on the basis of structural and sequence homology. RGS proteins contain an RGS box which allows them to interact with activated G-alpha subunits and increase the rate that the G-alpha subunit hydrolyses GTP to GDP. The net effect of this interaction is to reduce the time that the G-alpha subunit actively signals to other proteins. As well as their characteristic RGS box domains, RGS proteins often contain other protein-protein interaction domains such as PDZ domains on RGS-R12 members. These protein-protein interaction domains make RGS proteins the target of considerable research efforts because of the implication that RGS proteins can, like arrestins, act as signalling intermediates as well as their role in regulating G-alpha signalling. For example, RGS proteins containing the RBD domain have been shown to initiate MAPK signalling [17]
The roles of RGS proteins in mu-opioid receptor signalling have been quite extensively studied, and examples of these studies are demonstrative of general RGS function. RGS2 and RGS3, for example, increase opioid agonist potency, while RGS4 and RGS16 reduce the potency of agonists. It is not known whether RGS2 and RGS3 actively reduce the rate of G-alpha GTP hydrolysis, or whether their effect is mediated by one of their other protein-protein interactions [18]. Experiments in which RGS9-2 is knocked out show increased response to Mu-opioid agonists and impaired desensitisation [18]. Garzon et al. in a 2004 paper [17] demonstrated that morphine “alters the selective association between mu-opioid receptors and specific RGS proteins in mouse periaqueductal gray matter,” and in pull-down assays, they noted that certain proteins increased or decreased in their association with mu opioid receptors. It is possible that this may be something to do with morphine altering the receptor conformation and subsequently the network of proteins, particularly G-proteins, which interact with it. RGS proteins have selectivity for specific G-proteins, and if the group of G-proteins present in the network changes, then the group of RGS proteins present would also be likely to change.

Homer:

Several metabotropic glutamate receptors, such as mGluR1a and mGluR5a & b, along with Ca++ permeable IP3 receptors, ryanodine receptors, TRP channels, dynamin II and shank proteins contain the sequence (-PPxxFR-) which is a binding sequence for ‘Homer’ proteins. These Homer proteins, contain an enabled “VASP homology-like” domain which binds to the Homer binding sequence, and a C-terminal coiled coil domain which allows them to homo- and heteromultimerise. It is Homer’s coiled coil interactions that allow the above proteins to form large complexes. A complex containing mGluR’s, Homer proteins, TRP channels, ryanodine receptors and P/Q Ca++ channels, according to Bockaert et al. [4] would “constitute an ideal machinery for intracellular Ca++ release.” Homer proteins act primarily as scaffolding for protein complex formation, but experiments inhibiting Homer activity by using Homer1a, which lacks the coiled coil domain and acts as a dominant negative form of Homer, have shown that Homer has regulatory effects on mGluR signalling and ryanodine channel function [4].

GPCR-GPCR Interactions:

GIPs are essential to the function of a receptosome, but it is important to note that GPCRs do not just interact with non-GPCR proteins: the recent literature [19] documents many experiments exploring GPCR-GPCR interactions, including homo- and hetero-oligomerisation. Oligomerisation of GPCRs can affect many properties of GPCR function or sometimes only one or none, depending on the particular oligomer. For example, heteromeric complexes of B2ARs and delta or kappa opioid receptors doesn’t affect the pharmacology of either the adrenergic or opioidergic units, but profoundly alters the trafficking properties of the heteromer [20]. Again a familiar concept can be found in the nature of GPCR oligomerisation: signalling specificity and complexity are increased by a further level.

RAMPs:

The discovery of Receptor Activity Modifying Proteins or RAMPs revolutionised the field of GPCR signalling, because it demonstrated that not only could GIPs fine tune GPCR signalling, modulate trafficking and activate secondary signalling pathways, they could also turn a receptor into a completely different entity, with a totally different cognate ligand. There are three members of the RAMP family that have been identified so far, designated RAMP1, 2 & 3. As an example of RAMP function, RAMP1 can bind to the calcitonin receptor-like receptor (CL,) and convert it into a ‘high affinity calcitonin gene-related peptide receptor.” Alternatively, interaction of CL with RAMP2 or 3 produces an adrenomedullin receptor. RAMPs are now known to interact with the majority of GPCR Class II receptors, and are regulated heavily by physiological and pathophysiological processes. For example, RAMP2 and adrenomedullin mRNA are elevated in models of cardiac hypotrophy, and during pregnancy, progesterone causes upregulation of all three RAMPs. It is also thought that many of the orphan ligands which have been found (i.e. no receptor has been identified,) are ligands to GPCR-RAMP complexes, when the GPCR already has a cognate ligand in its non-RAMP complexed state [20].

‘Magic tail’ interacting proteins & PDZ Domains:

The C-terminal tail of many GPCRs contains a PDZ ligand, to which proteins with the PDZ protein-protein interaction domain common to many proteins involved in receptor signalling bind [21]. The protein PICK1 (Protein Interacting with C-kinase 1) is one such protein: by binding to the PDZ ligand motif of mGluR7a, PICK1 induces clustering of these receptors at presynaptic terminals. It is also proposed that PICK1 interaction with mGluR7a receptors mediates coupling to Ca++ channels [22].
The protein NHERF (Na+/H+ exchange regulatory factor) also contains PDZ domains, and controls the signalling properties of parathyroid hormone receptor, which binds to NHERF by its PDZ ligand. PDZ-ligand mediated coupling of NHERF to B2AR’s and kappa opioid receptors is likely the way that NHERF controls the Na+/H+ exchanger protein [21,23].
PDZ-ligand interactions between the PDZ domain of cyclic nucleotide Ras guanine exchange factor and the PDZ ligand on B2A enables B2AR to activate Ras and the associated MAPK pathway [21].
PDZ-ligand interactions also play an important role in receptosome scaffolding. The protein Shank spatially organises receptors and ion channels and provides interaction between receptors and the cytoskeleton [21]

Methods: Proteomics and experimental determination of protein-protein interactions

The dawn of the new millennium has seen the development of high throughput methods which generate vast amounts of novel data on protein-protein interactions. A number of different methods have been used to generate this data, all with their respective advantages and limitations. Use of different methods, or even variations of conditions within methods, can produce conflicting data sets. Appropriate synthesis of data sets produced by different methods is required to produce a coherent ‘map’ of interactions.

Researchers studying protein-protein interactions have a large toolbox of methodologies at their disposal. These include complementation assays, mass spectrometric approaches, chip based methods and bioinformatic analysis. The nature of the data produced by various methods differs: data can be qualitative or quantitative, and can describe pairwise interactions between two interaction partners, or can describe grouped interactions within a complex. The inability of most methods used to investigate large scale interactomes to measure quantitative information about interactions such as kinetics raises an important question: what exactly constitutes an interaction? Some biologically relevant interactions may occur on short timescales with very low affinity, but might be considered irrelevant by, or be below the sensitivity of such methods [25].

One issue that is particularly applicable to the study of protein-protein interactions occurring in receptosomes, and particularly interactions with membrane bound proteins, is the difficulty of resolving hydrophobic proteins in 2D gels [25,26]. Modern two-dimensional liquid chromatographic techniques have been able to provide improved resolution of hydrophobic proteins but preparation of pull-down assay experiments still proves difficult with membrane proteins [26]. One of the other important problems in GPCR and GIP interaction analysis is the low cellular concentrations of these proteins. If the experimenter chooses to overexpress a particular GPCR or GIP, they run the risk of ruining the stoichiometry of the interaction network [26].

One particularly effective method of analysing protein complexes is called SEAM, which stands for Sequential Epitope tagging, Affinity tagging and Mass spectrometry. In this process, a protein is selected and epitope tags such as Myc are fused to one of its termini. It is then overexpressed in a cellular system of choice and the cell lysate is run through an affinity column where anti-Myc antibodies are attached to the beads. A second mixture of proteins is then run through the column, and those proteins that are bound to the epitope tagged proteins are resolved by 2D liquid chromatography and fed into a mass spectrometer for identification. Subsequently, one of the MS identified proteins is then Myc tagged and the procedure run again. In this way, it is possible to build up complexes of proteins [27].

Obviously a vast amount of information has to be gathered regarding protein-protein interactions between components of signalling machinery before any kind of mathematical modelling process can be applied to these networks. First, it is necessary to determine the stoichiometry of each complex, and the precise interactions of each protein with each other. It would also be exceptionally useful to have the crystal or NMR structures of each protein involved. Additionally, having identified the qualitative aspects of the system, quantitative biophysical data would be needed concerning the strength and kinetics of interactions. This task will be a massive undertaking, but eventually researchers will be able to build these mathematical models of GPCR signalling and incorporate them into pre-existing models of human brain function, such as the Blue Brain project that is being run on IBM’s Blue Gene supercomputer (http://bluebrainproject.epfl.ch/). Once GPCR and GIP interactions can be comprehensively modelled, the potential for drug design targeted to, and therapeutic intervention of these systems will be unprescendented.



REFERENCES:

1.) Rang, Dale, Ritter, Moore “Pharmacology,” 5th Edition, Elsevier Sciences Ltd. 2003.
2.) Ostrom RS. “New determinants of receptor-effector coupling: trafficking and compartmentation in membrane microdomains.” Mol Pharmacol. 2002 Mar;61(3):473-6.
3.) Bockaert J, Roussignol G, Becamel C, Gavarini S, Joubert L, Dumuis A, Fagni L, Marin P. “GPCR-interacting proteins (GIPs): nature and functions.”
Biochem Soc Trans. 2004 Nov;32(Pt 5):851-5.
4.) Bockaert J, Fagni L, Dumuis A, Marin P. “GPCR interacting proteins (GIP).
Pharmacol Ther. 2004 Sep;103(3):203-21.”
5.) Ostrom RS, Insel PA. “The evolving role of lipid rafts and caveolae in G protein-coupled receptor signaling: implications for molecular pharmacology.”
Br J Pharmacol. 2004 Sep;143(2):235-45. Epub 2004 Aug 2.
6.) Kukkonen JP. “Regulation of receptor-coupling to (multiple) G proteins. A challenge for basic research and drug discovery.” Receptors Channels. 2004;10(5-6):167-83.
7.) Patel MB, Patel CN, Rajashekara V, Yoburn BC “Opioid agonists differentially regulate mu opioid receptors and trafficking proteins in vivo.” Mol Pharmacol. 2002 Dec;62(6):1464-70
8.) Hermans E. “Biochemical and pharmacological control of the multiplicity of coupling at G-protein-coupled receptors.” Pharmacol Ther. 2003 Jul;99(1):25-44.
9.) No reference: personal theory
10.) Hall RA, Premont RT, Lefkowitz RJ. “Heptahelical receptor signalling: beyond the G protein paradigm” J Cell Biol. 1999 May 31;145(5):927-32.
11.) Milano SK, Pace HC, Kim YM, Brenner C, Benovic JL. “Scaffolding functions of arrestin-2 revealed by crystal structure and mutagenesis.” Biochemistry. 2002 Mar 12;41(10):3321-8.
12.) Burgueno J, Enrich C, Canela EI, Mallol K, Lluis C, Franco R, Ciruela F “Metabotropic glutamate type 1 alpha receptor localizes in low-density caveolin-rich plasma membrane fractions” J Neurochem. 2003 Aug;86(4):785-91.
13.) Leclerc PC, Auger-Messier M, Lanctot PM, Escher E, Leduc R, Guillemette G. “A polyaromatic caveolin-binding-like motif in the cytoplasmic tail of the type 1 receptor for angiotensin II plays an important role in receptor trafficking and signaling.” Endocrinology. 2002 Dec;143(12):4702-10.
14.) Yamaguchi T, Murata Y, Fujiyoshi Y, Doi T. “Regulated interaction of endothelin B receptor with caveolin-1.” Eur J Biochem. 2003 Apr;270(8):1816-27.
15.) Inglese J, Freedman NJ, Koch WJ, Lefkowitz RJ “Structure and mechanism of G protein-coupled receptor kinases” J Biol Chem. 1993 Nov 15;268(32):23735-8.
16.) Lodish, Berk, Zipursky, Matsudaira, Baltimore, Darnell “Molecular Cell Biology” 4th Edition, p734-5 W.H. Freedman & Company 2000
17.) Garzon J, Rodriguez-Munoz M, Sanchez-Blazquez P. “Morphine alters the selective association between mu-opioid receptors and specific RGS proteins in mouse periaqueductal gray matter.” Neuropharmacology. 2005 May;48(6):853-68.
18.) Garzo n, J., Rodrıguez-Diaz, M., Lopez-Fando, A., Sanchez-
Blazquez, P. “RGS9 proteins facilitate acute tolerance
to mu-opioid effects.” European Journal of Neuroscience 13,
801-811. 2001
19.) Maggio R, Novi F, Scarselli M, Corsini GU “The impact of G-protein-coupled receptor hetero-oligomerization on function and pharmacology” FEBS J. 2005 Jun;272(12):2939-46.
20.) Jordan BA, Trapaidze N, Gomes I, Nivarthi R, Devi LA “Oligomerization of opioid receptors with B2-adrenergic receptors: a role in trafficking and mitogen-activated protein kinase activation” PNAS Jan 2, 2002 vol.98(1)343-348
21.) Morfis M, Christopoulos A, Sexton PM. “RAMPs: 5 years on, where to now?” Trends Pharmacol Sci. 2003 Nov;24(11):596-601.
22.) Boudin H, Doan A, Yia I, Shigemoto R, Huganir RL, Worley P, Craig AM. “Presynaptic clustering of mGluR7a requires the PICK1 PDZ domain binding site.” Neuron. 2000 Nov;28(2):485-97.
23.) Hall RA, Premont RT, Chow CW, Blitzer JT, Pitcher JA, Claing A, Stoffel RH, Barak LS, Shenolikar S, Weinman EJ, Grinstein S, Lefkowitz RJ. “The beta2-adrenergic receptor interacts with the Na+/H+-exchanger regulatory factor to control Na+/H+ exchange.” Nature. 1998 Apr 9;392(6676):626-30.
24.) Doronin S, Malbon CC. “Functional proteomics of G-protein-coupled receptors: analysis of large scale signalling devices” Pharmaceutical News (2002) 9, 347-355.
25.) von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P. “Comparative assessment of large-scale data sets of protein-protein interactions.”
Nature. 2002 May 23;417(6887):399-403. Epub 2002 May 8.
26.) Wu CC, Yates Jr. III “The application of mass spectrometry to membrane proteomics” Nat Biotechnol. 2003 Mar;21(3):262-7.
27.) Deshaies RJ, Seol JH, McDonald WH, Cope G, Lyapina S, Shevchenko A, Shevchenko A, Verma R, Yates JR 3rd. “Charting the protein complexome in yeast by mass spectrometry.” Mol Cell Proteomics. 2002 Jan;1(1):3-10.


This could use some proof-reading. I submitted it for marking about 10 minutes after I finished it late on a Sunday night (was due by the time Michelle got to work on Monday morning.) She's actually letting me proof it and resubmit it because its kinda ugly.
 
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Homeostatis in neuronal signalling

Homeostatis is the body’s tendency to keep everything the same, like a thermostat, but not just for temperature, but nearly every physiological parameter. Simple homeostatic controls exist for things like hormones, for instance the adrenal medullary cells which releases adrenaline, will have receptors for adrenaline, which slow adrenal release. This process is known as negative feedback. More complicated homeostatic controls exist for things such as body weight, blood pressure and water intake, however the general theme is the same, if a change takes the body away from its homeostatic set point, then negative feedback will try and restore this.

Nearly all aspects of signaling in the synapse are homeostatically controlled. The release of nearly all neurotransmitters are controlled by an autoreceptor, a presynaptic receptor which receives the type of neurotransmitter which the neuron it is located on releases, and feedbacks to inhibit the release of this neurotransmitter. Noradrenaline release is controlled by the alpha2 adrenoreceptor, GABA by the GABAB receptor, serotonin by the 5HT1A/B receptor, dopamine by the D2 receptor, glutamate by mGluRIII. When neurotransmitters bind to their autoreceptors, they usually activate a G-protein coupled cascade which leads to the inhibition of calcium channels and/or activation of potassium channels, which leads to a reduction in neurotransmitter release and presynaptic depolarization respectively. Many autoreceptors also inhibit the synthesis of their respective neurotransmitter. For instance the dopamine D2 receptor leads to phosphorylation and inhibition of tyrosine hydroxylase, the rate limiting enzyme in dopamine production. It is worth noting that these receptors are usually expressed in non-presynaptic locations, on other neuronal types, where they have a different function. Though sometimes they maintain their function as inhibitors of neurotransmitter release, for instance the histamine autoreceptor, the H3 receptor, is found presynaptically on nearly every type on neuron, and has been shown to inhibit the release of every neurotransmitter so far investigated. When a receptor is acting like this it is referred to as heteroreceptor, the 5-HT1A is another common heteroreceptor. In most native systems, neurotransmitters are being released most of the time, and hence the autoreceptors are tonically (i.e. continuously) active, constantly damping the release of neurotransmitter. Therefore, drugs which block autoreceptors, (e.g. yohimbine, and alpha2 adrenoreceptor antagonist) lead to a large increase in released neurotransmitter.

Autoreceptor action is exclusively a presynaptic method of homeostastis. Receptors themselves are capable of gating hyperactivity. Many ligand gated ion channels desensitize (i.e. they enter a state of low efficacy) in response to the application of an agonist, often on an extremely rapid time scale, the AMPA glutamate receptor desensitizes to 10% of maximum current within 10ms of saturating concentrations of agonist. Benzodiazepines rapidly induce tolerance to their behavioural effects. These drugs, which potentiate the action of GABA at the GABA-A receptor, produce molecular changes which mirror this tolerance. The exact mechanism remains unclear, but evidence shows that it is likely that prolonged benzodiazepine treatment renders GABA-A receptors insensitive to benzodiazepine modulation. It seems likely that this is primarily due to the receptor being pulled into an intracellular vesicle (internalization), presumabley after the action of a kinase. The receptor is then modulated in some way, possibly by removing benzodiazepine sensitive subunits, so that it is insensitive to benzodiazepines and returned to the membrane surface. It seems that only very high doses or very long treatments with benzodiazepines lead to a total decrease in GABA-A receptor number, and this may be through reduction in GABA-A receptor subunit mRNA expression[1].

G-protein coupled receptors (GPCR) are also subject to desensitization. Activation of GPCRs increases the activity of G-protein receptor kinases, which phosphorylate the receptor and decrease their signaling efficacy (usually through a decrease in ligand or G-protein affinity). This phosphorylation is reversible, but also allows the binding of proteins called arrestins to the intracellular side of the GPCR. Arrestins not only completely cut the GPCR off from activating G-proteins but they also allows the binding of other molecules, classical the clathrins, which internalize the receptor. Once internalized, (where receptor is separated from interactions with ligands) it awaits one of two fates, reinsertion back into the membrane, or degradation by protiolytic enzymes. Hence long term treatment with agonists lead to a long-term depletion in receptor number (down-regulation), which only the synthesis of new receptors can resolve. It is worth noting that GPCRs can also interact with genes, altering expression and after chronic agonist application sufficient to induces internalization it is common to note a decrease in the production no that receptors mRNA, which will also reduce receptor numbers and further slow recovery times.

desensitization.gif

Figure 1. Downregulation of a receptor. If a receptor is occupied by an agonist (A), then it may be phosphorylated (P) by GPCR Kinase (GPK), which can lead to arrestin binding, and internalization.

References

[1] Bateson AN. Basic pharmacologic mechanisms involved in benzodiazepine tolerance and withdrawal. Curr Pharm Des. 2002;8(1):5-21
 
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BilZ0r said:
Note: I need people to rip into that one extra hard, if you see any problem with it, please tell me.

It's pretty good, except when you start talking about synaptic plasticity in the mesocorticolimbic dopamine system. It seems as if you have based your assertions mainly on the work of Reynolds, Hyland, and Wickens, but a lot of other people have looked at this, and it's safe to say there is absolutely NO consensus in the field. Despite valiant efforts by many labs, the role of dopamine in corticostriatal synaptic plasticity is still very mysterious. Some goups have observed LTD upon application of dopamine or dopamine agonists, and some groups have observed LTP. Adding another wrinkle to the confusion, the plasticity observed can be highly dependent on the stimulation protocol utilized. However, the role of striatal medium spiny neuron UP and DOWN states has not been fully explored. There are some tantalizing unpublished results from my old lab that take UP/DOWN acivity states into account and seem to explain some of other peoples' results, but they are still only a start. A grad student in my old lab is devoting all her energy to looking at this problem, but is constantly bitching about the inconsistency of her results. For an introductory text like this, I would avoid going into details and just say that dopamine appears to be involved in corticostriatal synaptic plasticity, which in turn seems to be important for reward-related learning, but nobody knows just HOW.
 
One suggestion:

This would be PERFECT in .pdf format when it's done. It could easily be distributed and shared amongst the community online. When this is done, it will be a major reference for all those with an amateur interest in pharmacology!
 
I think the in vivo evidence it pretty good, and I think that as a hypothesis it is undoubtabley the best. Meanwhile, as I say "While these hypothesis is certainly a vast over simplification, the basic rules are probably true. Dopamine increase synaptic plasticity, and so helps associations form between stimuli and behavior that lead to reward. When this system is hijacked by addictive drugs an aberrant association which encourages drug taking is formed."
 
^^^yeah, but does it promote LTP, or LTD??? You're making a HUGE stretch when you say there is "convincing" evidence that dopamine increases corticostriatal LTP. I would definitely agree that the evidence suggests that it promotes synaptic plasticity, but the direction, and the other necessary conditions under which plasticity in either direction can occur, are controversial to say the least. Probably dopamine can facilitate synaptic plasticity in either direction, depending on variables such as the activity state of the neuron and network and the presence of other neuromodulators. Again, I would advise you to avoid making definitive statments about such a thorny issue. Just "there is evidence that DA promotes synaptic plasticity" should be enough for an introductory treatment of the topic.

Also, what in vivo evidence are you referring to? Are you talking about slice work, or actual in vivo recordings in the intact brain?
 
Of course I'm talking about in vivo, as in, in vivo... whole alive animal.. Show me a paper which shows that dopamine premotes LTD in vivo... Sure, i'm relying heavily on the evidence from one group, but that's cause they're about the only people doing it.... as far as I am awear.
 
From newsweek, to even CNN. lately.
Even though they rarely use the term "Synapse" and its important role.
More and more, people are understanding finally the key roles that play into the how and why of the neuron.


Glad to see, there was a thread, to include this frequently overlooked and assumed function.


-Synapses, the spaces between neurons, are the channels through which we think, act, imagine, feel, and remember, and also the means by which our most fundamental traits, preferences, and beliefs are encoded. In short, they enable each of us to function as a single, integrated individual - a synaptic self - from moment to moment, from year to year-


Synaptic-Self - Joseph E. LeDoux
 
BilZ0r said:
Of course I'm talking about in vivo, as in, in vivo... whole alive animal..

Sorry, there are people who misuse in vivo to describe slice work, when that should really be called ex vivo. Also, I haven't read the Reynolds et al. Nature paper in about a year, so I was rusty on the details (just skimmed it again).

Bilz0r said:
Show me a paper which shows that dopamine premotes LTD in vivo... Sure, i'm relying heavily on the evidence from one group, but that's cause they're about the only people doing it.... as far as I am awear

There is none. As far as I know, Reynolds et al., 2001 is the only one where they look at synaptic plasticity induced by dopamine neuron stimulation (as opposed to addictive drugs). To my knowledge, nobody has replicated that finding, and word on the street is that other people are trying or have tried (however, this is second hand information from a prominent investigator in the field who may not believe the results in that paper).

On the other hand, it has been reported that repeated passive cocaine administration induces LTD in the nucleus accumbens (Thomas MJ, Beurrier C, Malenka RC, Nat. Neurosci 2001). There are also unpublished results from another lab that cocaine self-administration also induces LTD in the NAc. So there may be differences between the plasticity induced by ICSS and addictive drugs. These other results may show, albeit indirectly, that you can not unequivocally say that dopamine always promotes corticostriatal LTP.
 
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Yeah... but thats paper isn't looking at dopamines modulation of LTP or LTD, it's just looking at generaly glutamatergic tone... I don't find the results all together surprising, though interesting... no change in minature rates, indicating that there has been no change in total synapse number.... but changes in the electrically evoked signal caused by a decrease in quantal size.... that's pretty fucked up.... but yeah, that papers is looking at the effect of long term dopamine on glutamatergic signalling, not on the second to second effect of dopamine on synaptic plasticity.

I've said before that people should look to see the effect of dopamine (From SNc stimulation) on place cell activity... burst the dopamine when they're in the location of the place cell firing, and see whether the cells tonic or max firing changes...

And if they can only find goal cells in the Prefrontal cortex, then a study like that could get reallly interesting....
 
BilZ0r said:
And if they can only find goal cells in the Prefrontal cortex, then a study like that could get reallly interesting....

One group has, and here is the reference!
 
Awsome, I knew they'd be in there someone... Further evidence that the anterior cingulate is massively different from prelimbic and infralimbic cortices.

But I'll consider your advise and try and make it sounds a little less conclusive.
 
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